A major automotive software provider is in the business of consolidating data from different dealerships that spawns across various Dealer Management Systems, different connection types and storage systems. A Dealer Management System is a software suite that provides the tools auto dealers need to more effectively run their business. The dealership system keeps record of every service activity and workforce involved in delivery. All details related to vehicles in the inventory, on hold, PDI, allotted, display, location and branch wise distribution is present in the system. It also controls the quality of services provided by the dealers. The data collected by the software provided an array of information but not limited to vehicle identification information including VIN, Make, Model, Year , history of maintenance, sale and purchase information, repair history that may be collected to serve the customers. The platform uses the data to provide pricing intelligence, and help consumers in purchase decisions and providing true value price for the industry.
Data coming from various sources are in different format, type and had lots of data challenges – some to name are around duplication, typos , nulls and blanks when data is not there, codes vs abbreviation. Name also had various challenges around missing spaces, double names, transliteration, nicknames, diacritics and before deriving insights, the technical team was challenged with huge amount of time investment on the data preparation for analytics initiative. The complexity in the data quality and cleansing process as part of data preparation was the challenge the firm was facing. The firm needed a solution that adapts to evolving data types, various use cases around automotive and a modern streaming, real time modern data preparation and data quality, curation, integration platform and practices along with smart profiling using AI/ML and NLP technologies. Not only that, they wanted the visibility in terms of tracking different sources over a period of time and its quality measures.
DQLabs delivered precise, automated master customer data by consolidating Sales, Inventory, Vehicle, and Service data. The simple but interactive UI portal allowed the data stewards to easily interact with the incoming sources, and also participate in the learning process by approving, rejecting, or discarding changes to customer data; Every interaction of the data steward is taken into the reinforcement learning component of DQLabs to more power and make more automated decisions easily with a complete audit trail to ensure high data quality and compliance. Our solution enabled the firm to create highly accurate, comprehensive data profiles, including streaming digital behavior and IoT (Internet of Things) data, to drive the highest quality interactions with customers and streamline their analytics for pricing intelligence, and user behavior and other characteristics involved in purchase decisions.
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